Kidney Renal Papillary Cell Carcinoma: Correlation between miRseq expression and clinical features
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 501 genes and 8 clinical features across 101 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 3 genes correlated to 'Time to Death'.

    • HSA-MIR-141 ,  HSA-MIR-200C ,  HSA-MIR-937

  • 9 genes correlated to 'PATHOLOGY.T'.

    • HSA-MIR-1293 ,  HSA-MIR-217 ,  HSA-MIR-200A ,  HSA-MIR-452 ,  HSA-MIR-224 ,  ...

  • 7 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • HSA-MIR-3607 ,  HSA-MIR-3647 ,  HSA-MIR-1245 ,  HSA-MIR-16-1 ,  HSA-MIR-424 ,  ...

  • 10 genes correlated to 'TUMOR.STAGE'.

    • HSA-MIR-224 ,  HSA-MIR-200A ,  HSA-MIR-452 ,  HSA-MIR-200B ,  HSA-MIR-216A ,  ...

  • No genes correlated to 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test N=3 shorter survival N=3 longer survival N=0
AGE Spearman correlation test   N=0        
GENDER t test   N=0        
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
PATHOLOGY T Spearman correlation test N=9 higher pT N=6 lower pT N=3
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=7        
TUMOR STAGE Spearman correlation test N=10 higher stage N=7 lower stage N=3
Clinical variable #1: 'Time to Death'

3 genes related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-182.7 (median=13.9)
  censored N = 80
  death N = 14
     
  Significant markers N = 3
  associated with shorter survival 3
  associated with longer survival 0
List of 3 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of 3 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
HSA-MIR-141 1.66 1.807e-05 0.0091 0.762
HSA-MIR-200C 1.56 3.342e-05 0.017 0.75
HSA-MIR-937 4.1 8.872e-05 0.044 0.849

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-141 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.81e-05 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

Table S3.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 59.67 (12)
  Significant markers N = 0
Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 32
  MALE 69
     
  Significant markers N = 0
Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 87.62 (24)
  Score N
  0 1
  40 1
  90 10
  100 9
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.T'

9 genes related to 'PATHOLOGY.T'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.77 (0.93)
  N
  T1 56
  T2 13
  T3 31
  T4 1
     
  Significant markers N = 9
  pos. correlated 6
  neg. correlated 3
List of 9 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S7.  Get Full Table List of 9 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-1293 0.511 1.094e-06 0.000548
HSA-MIR-217 0.4743 1.374e-06 0.000687
HSA-MIR-200A -0.4491 2.469e-06 0.00123
HSA-MIR-452 0.4485 2.555e-06 0.00127
HSA-MIR-224 0.4473 3.079e-06 0.00153
HSA-MIR-216A 0.5643 3.962e-06 0.00197
HSA-MIR-200B -0.4172 1.42e-05 0.00703
HSA-MIR-92A-2 0.3918 5.083e-05 0.0251
HSA-MIR-429 -0.3803 8.748e-05 0.0431

Figure S2.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1293 to 'PATHOLOGY.T'. P value = 1.09e-06 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N'

PATHOLOGY.N Mean (SD) 0.54 (0.7)
  N
  N0 20
  N1 11
  N2 4
     
  Significant markers N = 0
Clinical variable #7: 'PATHOLOGICSPREAD(M)'

7 genes related to 'PATHOLOGICSPREAD(M)'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) Labels N
  M0 54
  M1 5
  MX 33
     
  Significant markers N = 7
List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S10.  Get Full Table List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
HSA-MIR-3607 1.387e-07 6.95e-05
HSA-MIR-3647 6.347e-07 0.000317
HSA-MIR-1245 6.938e-06 0.00346
HSA-MIR-16-1 5.036e-05 0.0251
HSA-MIR-424 5.911e-05 0.0294
HSA-MIR-1248 5.966e-05 0.0296
HSA-MIR-126 6.731e-05 0.0333

Figure S3.  Get High-res Image As an example, this figure shows the association of HSA-MIR-3607 to 'PATHOLOGICSPREAD(M)'. P value = 1.39e-07 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

10 genes related to 'TUMOR.STAGE'.

Table S11.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.89 (1.1)
  N
  Stage 1 51
  Stage 2 7
  Stage 3 23
  Stage 4 9
     
  Significant markers N = 10
  pos. correlated 7
  neg. correlated 3
List of 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S12.  Get Full Table List of 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-224 0.5389 5.096e-08 2.55e-05
HSA-MIR-200A -0.5296 7.997e-08 4e-05
HSA-MIR-452 0.5258 1.025e-07 5.12e-05
HSA-MIR-200B -0.4901 9.418e-07 0.000469
HSA-MIR-216A 0.5988 1.09e-06 0.000542
HSA-MIR-217 0.4871 1.719e-06 0.000853
HSA-MIR-429 -0.4586 5.475e-06 0.00271
HSA-MIR-1293 0.487 1.081e-05 0.00534
HSA-MIR-92A-2 0.4306 2.275e-05 0.0112
HSA-MIR-1269 0.4294 7.856e-05 0.0387

Figure S4.  Get High-res Image As an example, this figure shows the association of HSA-MIR-224 to 'TUMOR.STAGE'. P value = 5.1e-08 with Spearman correlation analysis.

Methods & Data
Input

  • Expresson data file = KIRP-TP.miRseq_RPKM_log2.txt

  • Clinical data file = KIRP-TP.clin.merged.picked.txt

  • Number of patients = 101

  • Number of genes = 501

  • Number of clinical features = 8

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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